Tools   Select Another Resource Report Type

http://www.nitrc.org/projects/topographica/

A software package for computational modeling of neural maps developed as part of the NIMH Human Brain Project. Topographica focuses on the large-scale structure and function that is visible only when many thousands of such neurons are connected into topographic maps containing millions of connections. The software package provides a general-purpose framework for building models at this level, at an appropriate level of detail and complexity, as determined by the available computing power, phenomena of interest, and amount of biological data available for validation. It is intended to complement low-level neuron simulators that are available, such as General Neural Simulation System and NEURON.

Proper citation: Topographica (RRID:SCR_014174) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/dfviewer/

A tool for visualizing displacement fields estimated in association with image registration. Based on the displacement vector field, a mesh is generated for visualization. The mesh can be color mapped with the jacobian determinant at each point for better localization of regions that undergo compression or expansion. Other key features include: view synchronization, adjustable mesh resolution, and conversion from deformation and HAMMER displacement fields.

Proper citation: Displacement Field Viewer (RRID:SCR_014101) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/friend

A GUI-based software for real-time fMRI processing, multivoxel pattern decoding and neurofeedback. The package integrates routines for image preprocessing in real-time, ROI-based feedback and brain decoding-based feedback using the FSL and libSVM libraries. Users can create or employ pre-specified visual stimuli for neurofeedback experiments. FRIEND can be used as Windows standalone software or as a multiplatform toolbox (called FRIEND Engine).

Proper citation: Functional Real-time Interactive Endogenous Neuromodulation and Decoding (FRIEND) (RRID:SCR_014186) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/fmpm

A tool which implements a functional analysis pipeline for the joint analysis of longitudinally measured functional data and clinical data (for example age, gender and disease status). FMPM consists of a functional mixed effects model for characterizing the association of functional response with covariates of interest by incorporating complex spatial–temporal correlation structure, an efficient method for spatially smoothing varying coefficient functions, an estimation method for estimating the spatial– temporal correlation structure, a test procedure with local and global test statistics for testing hypotheses of interest associated with functional response, and a simultaneous confidence band for quantifying the uncertainty in the estimated coefficient functions.

Proper citation: Functional Mixed Processes Models (RRID:SCR_014187) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/xnbc/

A full featured and extensible application which simulates biological neural networks using graphic tools which edit neurons and networks, run the simulation and analyze results. It is written in C and runs on Unix and Windows. It is specifically targeted for neuroscientists who are less experienced with computer programming.

Proper citation: XNBC (RRID:SCR_014182) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/sim_dwi_brain/

This resource provides simulated DW-MRI brain images and quantitative tools for evaluating the performance of diffusion analysis methods in terms of fiber orientation estimation and false-positive/-negative fiber rates, which are of fundamental importance to tractography based studies. DW data was generated using a multi-tensor model at SNRs of 9, 18 and 36, for sets of 20, 30, 40, 60, 90 and 120 gradient directions. For each combination of SNR and gradient direction set, 10 realizations of data are provided. All data is simulated with a diffusion-weighting of b=1000, as is common for clinical acquisitions.

Proper citation: Simulated DW-MRI Brain Data Sets for Quantitative Evaluation of Estimated Fiber Orientations (RRID:SCR_014168) Copy   

•   Source: SciCrunch Registry

http://www.theuais.org

A topical portal for the UAIS Lab of Lanzhou University which researches predicting depression and schizophrenia based on demographics and physiological information (EEG, ERPs, Genetics, MRI, fMRI, etc.). It also researches wearable bio-signal sensors and antennas, bio-signal processing, speech analysis, pervasive mental health, psycho-physiological computing, bioinformatics and multimodal data fusion and modeling.

Proper citation: Prediction and Diagnosis for Depression and Schizophrenia (RRID:SCR_014161) Copy   

•   Source: SciCrunch Registry

http://www.brainvoyager.com/products/brainviewer.html

Software that supports browsing and inspecting essential BrainVoyager data files as well as the header and content of DICOM files. The Viewer supports standard image files (JPEG, GIF, PNG, TIFF, BMP) allowing to inspect snapshots, figures or photos. Users can prepare a folder with selected data of a subject (VMRs, SRFs, Maps, snapshot images), which allows participants of fMRI measurements to browse their brain data and to show it to others. The Viewer can be handed over to colleagues not having a BrainVoyager license together with relevant data. This will allow them to view and explore your analyzed data files.

Proper citation: BrainVoyager Brain Viewer (RRID:SCR_006755) Copy   

•   Source: SciCrunch Registry

http://www.nitrc.org/projects/randomwalks/

A simple interface to simulate Brownian motion in arbitrary, complex environments. The analysis routines enable visualization of these models with DTI, q-space, and higher order diffusion weighted MRI.

Proper citation: DW-MRI Random Walk Simulator (RRID:SCR_006652) Copy   

•   Source: SciCrunch Registry

http://www.med.unc.edu/bric/ideagroup/free-softwares/mabmis

This software package implements an algorithm for accurate and consistent segmentation / labeling on a group of images. The images should be in Analyze format with paired header and image files. All images should be preprocessed so that they have been affinely aligned together.

Proper citation: MABMIS: Multi-Atlas Based Multi-Image Segmentation (RRID:SCR_006975) Copy   

•   Source: SciCrunch Registry

http://www.brain.org.au/software/mrtrix/

A set of tools to perform diffusion-weighted MRI white matter tractography in the presence of crossing fibres, using Constrained Spherical Deconvolution (Tournier et al.. 2004; Tournier et al. 2007), and a probabilisitic streamlines algorithm (e.g. Behrens et al., 2003; Parker et al., 2003). These applications have been written from scratch in C++, using the functionality provided by the GNU Scientific Library, and gtkmm. The software is currently capable of handling DICOM, NIfTI and AnalyseAVW image formats, amongst others. Installation * Unix/Linux * Microsoft Windows * Mac Os X, THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 16,2025.

Proper citation: MRtrix (RRID:SCR_006971) Copy   

•   Source: SciCrunch Registry

http://www.ebire.org/hcnlab/software/vamca.html

A stand-alone, open source human cortical meta-analysis and visualization toolbox for MatLab. It projects stereotaxic coordinates to a mean cortical surface by using an anatomical database of 60 young adults to provide multiple mappings of normalized cortical surfaces into MNI space. VAMCA performs the following analyses: # Multi-Fiducial Projection Mapping: Map stereotaxic 3D coordinates to the normalized cortical location for each of 60 database subjects. # Computing Centroid Locations for groups of foci both on a mean cortical surface and in MNI space. # Comparing Two Groups of Foci for differences in location (surface or 3D) of their group centroids and computing the groups' overlap extent using permutation tests. # Detecting Significant Densities of Foci or Density Differences of Two Groups within anatomical ROIs on a mean cortical surface by using Monte Carlo analyses. Coordinate weights allow fixed or random effects type analyses.

Proper citation: VAMCA (RRID:SCR_007028) Copy   

•   Source: SciCrunch Registry

http://brainmaps.org

An interactive multiresolution brain atlas that is based on over 20 million megapixels of sub-micron resolution, annotated, scanned images of serial sections of both primate and non-primate brains and integrated with a high-speed database for querying and retrieving data about brain structure and function. Currently featured are complete brain atlas datasets for various species, including Macaca mulatta, Chlorocebus aethiops, Felis catus, Mus musculus, Rattus norvegicus, Tyto alba and many other vertebrates. BrainMaps is currently accepting histochemical, immunocytochemical, and tracer connectivity data, preferably whole-brain. In addition, they are interested in EM, MRI, and DTI data.

Proper citation: BrainMaps.org (RRID:SCR_006878) Copy   

•   Source: SciCrunch Registry

http://www.mlnl.cs.ucl.ac.uk/pronto/

A software toolbox based on pattern recognition techniques for the analysis of neuroimaging data. Statistical pattern recognition is a field within the area of machine learning which is concerned with automatic discovery of regularities in data through the use of computer algorithms, and with the use of these regularities to take actions such as classifying the data into different categories. In PRoNTo, brain scans are treated as spatial patterns and statistical learning models are used to identify statistical properties of the data that can be used to discriminate between experimental conditions or groups of subjects (classification models) or to predict a continuous measure (regression models).

Proper citation: PRoNTo (RRID:SCR_006908) Copy   

•   Source: SciCrunch Registry

http://humanconnectome.org/consortia/

Project to map the neural pathways that underlie human brain function for several modalities of neuroimaging data including fMRI. The purpose of the Project is to acquire and share data about the structural and functional connectivity of the human brain. It will greatly advance the capabilities for imaging and analyzing brain connections, resulting in improved sensitivity, resolution, and utility, thereby accelerating progress in the emerging field of human connectomics. Altogether, the Human Connectome Project will lead to major advances in the understanding of what makes us uniquely human and will set the stage for future studies of abnormal brain circuits in many neurological and psychiatric disorders. The sixteen institutes and centers of the NIH Blueprint for Neuroscience have funded two major grants that will take complementary approaches to deciphering the brain's amazingly complex wiring diagram. An 11-institution consortium led by Washington University in St. Louis and the University of Minnesota received a 5-year grant to enable development and utilization of advanced Magnetic Resonance Imaging (MRI) methods to chart brain circuitry. A consortium led by Massachusetts General Hospital and the University of California at Los Angeles received a grant to enable building and refining a next-generation 3T MR scanner that improves the quality and spatial resolution with which brain connectivity data can be acquired at this field strength.

Proper citation: NIH Human Connectome Project (RRID:SCR_006942) Copy   

•   Source: SciCrunch Registry

http://www.wholebraincatalog.org/

THIS RESOURCE IS NO LONGER IN SERVICE, documented May 26, 2016. An open source, downloadable, 3d atlas of the mouse brain and its cellular constituents that allows multi-scale data to be visualized in a seamless way, similar to Google earth. Data within the Catalog is marked up with annotations and can link out to additional data sources via a semantic framework. This next generation open environment has been developed to connect members of the neuroscience community to facilitate solutions for today's intractable challenges in brain research through cooperation and crowd sourcing. The client-server platform provides rich 3-D views for researchers to zoom in, out, and around structures deep in a multi-scale spatial framework of the mouse brain. An open-source, 3-D graphics engine used in graphics-intensive computer gaming generates high-resolution visualizations that bring data to life through biological simulations and animations. Within the Catalog, researchers can view and contribute a wide range of data including: * 3D meshes of subcellular scenes or brain region territories * Large 2D image datasets from both electron and light level microscopy * NeuroML and Neurolucida neuronal reconstructions * Protein Database molecular structures Users of the Whole Brain Catalog can: * Fit data of any scale into the international standard atlas coordinate system for spatial brain mapping, the Waxholm Space. * View brain slices, neurons and their animation, neuropil reconstructions, and molecules in appropriate locations * View data up close and at a high resolution * View their own data in the Whole Brain Catalog environment * View data within a semantic environment supported by vocabularies from the Neuroscience Information Framework (NIF) at http://www.neuinfo.org. * Contribute code and connect personal tools to the environment * Make new connections with related research and researchers 5 Easy Ways to Explore: * Explore the datasets across multiple scales. * View data closely at high resolution. * Observe accurately simulated neurons. * Readily search for content. * Contribute your own research.

Proper citation: Whole Brain Catalog (RRID:SCR_007011) Copy   

•   Source: SciCrunch Registry

http://mcx.sourceforge.net/

A Monte Carlo simulation software for photon migration in 3D turbid media. It uses Graphics Processing Units (GPU) based massively parallel computing techniques and is extremely fast compared to the traditional single-threaded CPU-based simulations. Using an nVidia 8800GT graphics card (14MP/114Cores), the acceleration is about 300x~400x compared to a single core of Xeon 5120 CPU; this ratio can be as high as 700x with a GTX 280 GPU and 1400x with a GTX 470.

Proper citation: Monte Carlo eXtreme (RRID:SCR_007001) Copy   

•   Source: SciCrunch Registry

http://www.birncommunity.org/collaborators/function-birn/

The FBIRN Federated Informatics Research Environment (FIRE) includes tools and methods for multi-site functional neuroimaging. This includes resources for data collection, storage, sharing and management, tracking, and analysis of large fMRI datasets. fBIRN is a national initiative to advance biomedical research through data sharing and online collaboration. BIRN provides data-sharing infrastructure, software tools, strategies and advisory services - all from a single source.

Proper citation: Function BIRN (RRID:SCR_007291) Copy   

•   Source: SciCrunch Registry

http://ncmir.ucsd.edu/downloads/manual_align_rts2000.shtm

Software program to adjust the alignment of two adjacent images. Allows to correct for any misalignment that may occur during auto-alignment step. Serves as a bootstrap to get the images in approximately the right place.

Proper citation: Manual Align RTS2000 (RRID:SCR_007107) Copy   

•   Source: SciCrunch Registry

http://www.brainvisa.info/

BrainVISA is a modular an customizable software platform built to host heterogeneous tools dedicated to neuroimaging research. Many toolboxes have already been developed for BrainVISA (T1 MRI, sulcal identification and morphometry, cortical surface analysis, diffusion imaging and tractography, fMRI, nuclear imaging, EEG and MEG, TMS, histology and autoradiography, etc.). Anatomist is a software for interactive visualization of multimodal data and for manipulation of structured 3D objects. It allows to build scenes that merge or combine images, meshes, regions of interest, fibers, textures, color palettes, referential changes, etc. A user can interact in 3D and in real time with the objects of an Anatomist scene: change point of view, select objects, add/suppress objects, change colors, draw regions of interests, do manual registration, etc. BrainVISA main features are: * Harmonization of communications between different software. For instance, BrainVISA toolboxes are using home-made software but also third-party software such as FreeSurfer, FSL, SPM, nipy, R-project, Matlab, etc. * Ontology-based data organization allowing database sharing and automation of mass of data analysis. * Fusion and interactive visualization of multimodal data (using Anatomist software). * Automatic generation of graphical user interfaces. * Workflow monitoring and data quality checking. * Full customization possible. * Runs on Linux, Mac and Windows. * Programming Language: C++, Python * Supported Data Format: ANALYZE, DICOM, GIfTI, MINC, NIfTI-1, Other Format

Proper citation: BrainVISA / Anatomist (RRID:SCR_007354) Copy   

•   Source: SciCrunch Registry